Modeling the Electromyogram Signal of Stimulated Biceps Brachii Muscle

Khodadadi, Vahid; Rahatabad, Fereidoun Nowshiravan; Sheikhani, Ali; Jafarnia Dabanloo, Nader

doi:10.30491/ijmr.2020.253244.1151

Modeling the Electromyogram Signal of Stimulated Biceps Brachii Muscle

Document Type : Narrative Review

Authors

Vahid khodadadi ¹

Fereidoun Nowshiravan Rahatabad ¹

Ali Sheikhani ¹

Nader Jafarnia Dabanloo ²

¹ Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran

10.30491/ijmr.2020.253244.1151

Abstract

Introduction: The usage of modeling methods has been proposed to achieve a better understanding of biological systems, so that some ambiguities about their function could be resolved. Accordingly, the present review was performed to model the electromyogram signal of stimulated biceps brachii muscle.
Methods: In this review study, a search was performed in databases of Emerald, Cochrane Library, MEEDLINE, EMBASE, Wiley, Scopus, and Magiran on papers published over the past 20 years. Papers that fulfilled all inclusion criteria were critically appraised in order to assess their quality. Out of the 66 papers extracted, eight original papers were included. The findings obtained from the papers were noted, and then underwent content analysis and categorization.
Results: Findings indicated that most of the performed studies had been modeled using cybernetic, robotic, regression, and neural network modeling methods. These physiological mathematical models model the physiological structure of the muscle based on a direct description of biomechanical, biological, and physiological characteristics of the system individually, which is difficult for obtaining many parameters.
Conclusion: Most of the models presented so far do not match reality and have errors. Thus, studies are required to design a model similar to a biological system with the properties of biological systems in order to reduce the modeling error.

Keywords

Modeling

Signal

Electromyogram

Biceps Brachii Muscle